Human in the loop model Predictive Control: an irrigation canal case study

In this work, we propose to expand the application of model predictive control (MPC) to problems in which there are human agents involved in the sensing and actuation processes. To this end, a new configuration of a control system structure that combines centralized predictive control and local operations is presented. Additional constraints are included in the optimization problem to take into account the mobility and the role of the operators over the prediction horizon. This new type of control system structure, referred to as Mobile Model Predictive Control (MoMPC), is tested on a linear model of a large scale irrigation canal and its performance is compared to centralized MPC and manual operation.